Evaluating BM3D and NBNet: A Comprehensive Study of Image Denoising Across Multiple Datasets
This is an incremental study evaluating existing methods on new datasets to guide denoising strategy selection in real-world applications.
This paper compared traditional BM3D and modern NBNet for image denoising across multiple datasets, finding that BM3D excels in blur challenges while NBNet is more effective in complex noise environments like under- and over-exposure.
This paper investigates image denoising, comparing traditional non-learning-based techniques, represented by Block-Matching 3D (BM3D), with modern learning-based methods, exemplified by NBNet. We assess these approaches across diverse datasets, including CURE-OR, CURE-TSR, SSID+, Set-12, and Chest-Xray, each presenting unique noise challenges. Our analysis employs seven Image Quality Assessment (IQA) metrics and examines the impact on object detection performance. We find that while BM3D excels in scenarios like blur challenges, NBNet is more effective in complex noise environments such as under-exposure and over-exposure. The study reveals the strengths and limitations of each method, providing insights into the effectiveness of different denoising strategies in varied real-world applications.